sandeep-krishnamurthy closed pull request #13160: Fix Sphinx python docstrings
URL: https://github.com/apache/incubator-mxnet/pull/13160
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diff --git a/docs/api/python/ndarray/sparse.md
b/docs/api/python/ndarray/sparse.md
index 2ade059a70c..acd5d2d4acc 100644
--- a/docs/api/python/ndarray/sparse.md
+++ b/docs/api/python/ndarray/sparse.md
@@ -582,7 +582,7 @@ We summarize the interface for each class in the following
sections.
:members: shape, context, dtype, stype, data, indices, indptr, copy,
copyto, as_in_context, asscipy, asnumpy, asscalar, astype, tostype, slice,
wait_to_read, zeros_like, round, rint, fix, floor, ceil, trunc, sin, tan,
arcsin, arctan, degrees, radians, sinh, tanh, arcsinh, arctanh, expm1, log1p,
sqrt, square, __neg__, sum, mean, norm, square, __getitem__, __setitem__,
check_format, abs, clip, sign
.. autoclass:: mxnet.ndarray.sparse.RowSparseNDArray
- :members: shape, context, dtype, stype, data, indices, copy, copyto,
as_in_context, asnumpy, asscalar, astype, tostype, wait_to_read, zeros_like,
round, rint, fix, floor, ceil, trunc, sin, tan, arcsin, arctan, degrees,
radians, sinh, tanh, arcsinh, arctanh, expm1, log1p, sqrt, square,
__negative__, norm, __getitem__, __setitem__, check_format, retain, abs, clip,
sign
+ :members: shape, context, dtype, stype, data, indices, copy, copyto,
as_in_context, asnumpy, asscalar, astype, tostype, wait_to_read, zeros_like,
round, rint, fix, floor, ceil, trunc, sin, tan, arcsin, arctan, degrees,
radians, sinh, tanh, arcsinh, arctanh, expm1, log1p, sqrt, square, norm,
__getitem__, __setitem__, check_format, retain, abs, clip, sign
.. automodule:: mxnet.ndarray.sparse
:members:
diff --git a/python/mxnet/gluon/model_zoo/vision/mobilenet.py
b/python/mxnet/gluon/model_zoo/vision/mobilenet.py
index 1a84e05af20..88610571252 100644
--- a/python/mxnet/gluon/model_zoo/vision/mobilenet.py
+++ b/python/mxnet/gluon/model_zoo/vision/mobilenet.py
@@ -62,7 +62,7 @@ def _add_conv_dw(out, dw_channels, channels, stride,
relu6=False):
class LinearBottleneck(nn.HybridBlock):
r"""LinearBottleneck used in MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
@@ -138,7 +138,7 @@ def hybrid_forward(self, F, x):
class MobileNetV2(nn.HybridBlock):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
@@ -223,7 +223,7 @@ def get_mobilenet_v2(multiplier, pretrained=False,
ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
@@ -269,7 +269,7 @@ def mobilenet1_0(**kwargs):
def mobilenet_v2_1_0(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
@@ -300,7 +300,7 @@ def mobilenet0_75(**kwargs):
def mobilenet_v2_0_75(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
@@ -331,7 +331,7 @@ def mobilenet0_5(**kwargs):
def mobilenet_v2_0_5(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
@@ -362,7 +362,7 @@ def mobilenet0_25(**kwargs):
def mobilenet_v2_0_25(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
- Mobile Networks for Classification, Detection and Segmentation"
+ Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381>`_ paper.
Parameters
diff --git a/python/mxnet/module/base_module.py
b/python/mxnet/module/base_module.py
index c534261eacc..babea53d6e4 100644
--- a/python/mxnet/module/base_module.py
+++ b/python/mxnet/module/base_module.py
@@ -279,8 +279,8 @@ def score(self, eval_data, eval_metric, num_batch=None,
batch_end_callback=None,
def iter_predict(self, eval_data, num_batch=None, reset=True,
sparse_row_id_fn=None):
"""Iterates over predictions.
- Example Usage:
- ----------
+ Examples
+ --------
>>> for pred, i_batch, batch in module.iter_predict(eval_data):
... # pred is a list of outputs from the module
... # i_batch is a integer
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index bf1140d2071..112fd56af67 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -399,7 +399,7 @@ def __setitem__(self, key, value):
Parameters
----------
- key : int, slice, list, np.ndarray, NDArray, or tuple of all previous
types
+ key : int, mxnet.ndarray.slice, list, np.ndarray, NDArray, or tuple of
all previous types
The indexing key.
value : scalar or array-like object that can be broadcast to the shape
of self[key]
The value to set.
@@ -467,7 +467,7 @@ def __getitem__(self, key):
Parameters
----------
- key : int, slice, list, np.ndarray, NDArray, or tuple of all previous
types
+ key : int, mxnet.ndarray.slice, list, np.ndarray, NDArray, or tuple of
all previous types
Indexing key.
Examples
@@ -2642,9 +2642,9 @@ def add(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be added.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
@@ -2704,9 +2704,9 @@ def subtract(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
@@ -2765,9 +2765,9 @@ def multiply(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
@@ -2826,9 +2826,9 @@ def divide(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array in division.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
@@ -2883,9 +2883,9 @@ def modulo(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array in modulo.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they
must be
broadcastable to a common shape.
@@ -3002,9 +3002,9 @@ def maximum(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3059,9 +3059,9 @@ def minimum(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3120,9 +3120,9 @@ def equal(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3184,9 +3184,9 @@ def not_equal(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3251,9 +3251,9 @@ def greater(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3315,9 +3315,9 @@ def greater_equal(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3379,9 +3379,9 @@ def lesser(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3443,9 +3443,9 @@ def lesser_equal(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First array to be compared.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must
be
broadcastable to a common shape.
@@ -3506,9 +3506,9 @@ def logical_and(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First input of the function.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they
must be
broadcastable to a common shape.
@@ -3566,9 +3566,9 @@ def logical_or(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First input of the function.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they
must be
broadcastable to a common shape.
@@ -3626,9 +3626,9 @@ def logical_xor(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.array
First input of the function.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they
must be
broadcastable to a common shape.
diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index 3d18a596d4f..1e69eac7f70 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -420,7 +420,7 @@ def __setitem__(self, key, value):
if isinstance(key, py_slice):
if key.step is not None or key.start is not None or key.stop is
not None:
raise ValueError('Assignment with slice for CSRNDArray is not
' \
- 'implmented yet.')
+ 'implemented yet.')
if isinstance(value, NDArray):
# avoid copying to itself
if value.handle is not self.handle:
@@ -1205,9 +1205,9 @@ def add(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.sparse.array
First array to be added.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.sparse.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
@@ -1277,9 +1277,9 @@ def subtract(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.sparse.array
First array to be subtracted.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.sparse.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.__spec__
@@ -1348,9 +1348,9 @@ def multiply(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.sparse.array
First array to be multiplied.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.sparse.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
@@ -1432,9 +1432,9 @@ def divide(lhs, rhs):
Parameters
----------
- lhs : scalar or array
+ lhs : scalar or mxnet.ndarray.sparse.array
First array in division.
- rhs : scalar or array
+ rhs : scalar or mxnet.ndarray.sparse.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
diff --git a/python/mxnet/optimizer/optimizer.py
b/python/mxnet/optimizer/optimizer.py
index bc03497fc99..d632a8c7c64 100644
--- a/python/mxnet/optimizer/optimizer.py
+++ b/python/mxnet/optimizer/optimizer.py
@@ -70,11 +70,12 @@ class Optimizer(object):
The initial number of updates.
multi_precision : bool, optional
- Flag to control the internal precision of the optimizer.
- ``False`` results in using the same precision as the weights (default),
- ``True`` makes internal 32-bit copy of the weights and applies gradients
- in 32-bit precision even if actual weights used in the model have lower
precision.
- Turning this on can improve convergence and accuracy when training with
float16.
+ Flag to control the internal precision of the optimizer.::
+
+ False: results in using the same precision as the weights (default),
+ True: makes internal 32-bit copy of the weights and applies
gradients
+ in 32-bit precision even if actual weights used in the model have
lower precision.
+ Turning this on can improve convergence and accuracy when training
with float16.
Properties
----------
@@ -481,16 +482,17 @@ class SGD(Optimizer):
Parameters
----------
momentum : float, optional
- The momentum value.
+ The momentum value.
lazy_update : bool, optional
- Default is True. If True, lazy updates are applied \
- if the storage types of weight and grad are both ``row_sparse``.
+ Default is True. If True, lazy updates are applied \
+ if the storage types of weight and grad are both ``row_sparse``.
multi_precision: bool, optional
- Flag to control the internal precision of the optimizer.
- ``False`` results in using the same precision as the weights (default),
- ``True`` makes internal 32-bit copy of the weights and applies
gradients \
- in 32-bit precision even if actual weights used in the model
have lower precision.\
- Turning this on can improve convergence and accuracy when
training with float16.
+ Flag to control the internal precision of the optimizer.::
+
+ False: results in using the same precision as the weights
(default),
+ True: makes internal 32-bit copy of the weights and applies
gradients
+ in 32-bit precision even if actual weights used in the model have
lower precision.
+ Turning this on can improve convergence and accuracy when training
with float16.
"""
def __init__(self, momentum=0.0, lazy_update=True, **kwargs):
super(SGD, self).__init__(**kwargs)
@@ -692,20 +694,21 @@ class LBSGD(Optimizer):
Parameters
----------
momentum : float, optional
- The momentum value.
+ The momentum value.
multi_precision: bool, optional
- Flag to control the internal precision of the optimizer.
- ``False`` results in using the same precision as the weights (default),
- ``True`` makes internal 32-bit copy of the weights and applies gradients
- in 32-bit precision even if actual weights used in the model
have lower precision.`<
- Turning this on can improve convergence and accuracy when
training with float16.
+ Flag to control the internal precision of the optimizer.::
+
+ False: results in using the same precision as the weights
(default),
+ True: makes internal 32-bit copy of the weights and applies
gradients
+ in 32-bit precision even if actual weights used in the model have
lower precision.
+ Turning this on can improve convergence and accuracy when training
with float16.
+
warmup_strategy: string ('linear', 'power2', 'sqrt'. , 'lars' default :
'linear')
warmup_epochs: unsigned, default: 5
batch_scale: unsigned, default: 1 (same as batch size*numworkers)
updates_per_epoch: updates_per_epoch (default: 32, Default might not
reflect true number batches per epoch. Used for warmup.)
begin_epoch: unsigned, default 0, starting epoch.
"""
-
def __init__(self, momentum=0.0, multi_precision=False,
warmup_strategy='linear',
warmup_epochs=5, batch_scale=1, updates_per_epoch=32,
begin_epoch=0, num_epochs=60,
**kwargs):
@@ -934,11 +937,12 @@ class NAG(Optimizer):
momentum : float, optional
The momentum value.
multi_precision: bool, optional
- Flag to control the internal precision of the optimizer.
- ``False`` results in using the same precision as the weights (default),
- ``True`` makes internal 32-bit copy of the weights and applies
gradients \
- in 32-bit precision even if actual weights used in the model
have lower precision.\
- Turning this on can improve convergence and accuracy when
training with float16.
+ Flag to control the internal precision of the optimizer.::
+
+ False: results in using the same precision as the weights
(default),
+ True: makes internal 32-bit copy of the weights and applies
gradients
+ in 32-bit precision even if actual weights used in the model have
lower precision.
+ Turning this on can improve convergence and accuracy when training
with float16.
"""
def __init__(self, momentum=0.0, **kwargs):
super(NAG, self).__init__(**kwargs)
@@ -1176,9 +1180,11 @@ class RMSProp(Optimizer):
epsilon : float, optional
Small value to avoid division by 0.
centered : bool, optional
- Flag to control which version of RMSProp to use.
- ``True`` will use Graves's version of `RMSProp`,
- ``False`` will use Tieleman & Hinton's version of `RMSProp`.
+ Flag to control which version of RMSProp to use.::
+
+ True: will use Graves's version of `RMSProp`,
+ False: will use Tieleman & Hinton's version of `RMSProp`.
+
clip_weights : float, optional
Clips weights into range ``[-clip_weights, clip_weights]``.
"""
diff --git a/python/mxnet/recordio.py b/python/mxnet/recordio.py
index 6fc4d8e7bf5..2def141c934 100644
--- a/python/mxnet/recordio.py
+++ b/python/mxnet/recordio.py
@@ -36,8 +36,8 @@
class MXRecordIO(object):
"""Reads/writes `RecordIO` data format, supporting sequential read and
write.
- Example usage:
- ----------
+ Examples
+ ---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
<mxnet.recordio.MXRecordIO object at 0x10ef40ed0>
>>> for i in range(5):
@@ -124,8 +124,8 @@ def reset(self):
If the record is opened with 'w', this function will truncate the file
to empty.
- Example usage:
- ----------
+ Examples
+ ---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(2):
... item = record.read()
@@ -143,8 +143,8 @@ def reset(self):
def write(self, buf):
"""Inserts a string buffer as a record.
- Example usage:
- ----------
+ Examples
+ ---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
@@ -163,8 +163,8 @@ def write(self, buf):
def read(self):
"""Returns record as a string.
- Example usage:
- ----------
+ Examples
+ ---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(5):
... item = record.read()
@@ -196,8 +196,8 @@ def read(self):
class MXIndexedRecordIO(MXRecordIO):
"""Reads/writes `RecordIO` data format, supporting random access.
- Example usage:
- ----------
+ Examples
+ ---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
@@ -261,8 +261,8 @@ def seek(self, idx):
def tell(self):
"""Returns the current position of write head.
- Example usage:
- ----------
+ Examples
+ ---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> print(record.tell())
0
@@ -283,8 +283,8 @@ def tell(self):
def read_idx(self, idx):
"""Returns the record at given index.
- Example usage:
- ----------
+ Examples
+ ---------
>>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w')
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
@@ -299,8 +299,8 @@ def read_idx(self, idx):
def write_idx(self, idx, buf):
"""Inserts input record at given index.
- Example usage:
- ----------
+ Examples
+ ---------
>>> for i in range(5):
... record.write_idx(i, 'record_%d'%i)
>>> record.close()
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 7ac63c6c53d..d23b563add9 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -261,10 +261,14 @@ def rand_sparse_ndarray(shape, stype, density=None,
dtype=None, distribution=Non
Parameters
----------
shape: list or tuple
- stype: str, valid values: "csr" or "row_sparse"
- density, optional: float, should be between 0 and 1
- distribution, optional: str, valid values: "uniform" or "powerlaw"
- dtype, optional: numpy.dtype, default value is None
+ stype: str
+ valid values: "csr" or "row_sparse"
+ density: float, optional
+ should be between 0 and 1
+ distribution: str, optional
+ valid values: "uniform" or "powerlaw"
+ dtype: numpy.dtype, optional
+ default value is None
Returns
-------
diff --git a/python/mxnet/visualization.py b/python/mxnet/visualization.py
index a0eb253cc7e..9297edee810 100644
--- a/python/mxnet/visualization.py
+++ b/python/mxnet/visualization.py
@@ -213,12 +213,14 @@ def plot_network(symbol, title="plot", save_format='pdf',
shape=None, node_attrs
input symbol names (str) to the corresponding tensor shape (tuple).
node_attrs: dict, optional
Specifies the attributes for nodes in the generated visualization.
`node_attrs` is
- a dictionary of Graphviz attribute names and values. For example,
- ``node_attrs={"shape":"oval","fixedsize":"false"}``
- will use oval shape for nodes and allow variable sized nodes in
the visualization.
+ a dictionary of Graphviz attribute names and values. For example::
+
+ node_attrs={"shape":"oval","fixedsize":"false"}
+
+ will use oval shape for nodes and allow variable sized nodes in the
visualization.
hide_weights: bool, optional
- If True (default), then inputs with names of form *_weight
(corresponding to weight
- tensors) or *_bias (corresponding to bias vectors) will be hidden for
a cleaner
+ If True (default), then inputs with names of form *_weight*
(corresponding to weight
+ tensors) or *_bias* (corresponding to bias vectors) will be hidden for
a cleaner
visualization.
Returns
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 77d9bf06e2d..0faa668caf9 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -396,9 +396,9 @@ The storage type of ``slice`` output depends on storage
types of inputs
- otherwise, ``slice`` generates output with default storage
.. note:: When input data storage type is csr, it only supports
-step=(), or step=(None,), or step=(1,) to generate a csr output.
-For other step parameter values, it falls back to slicing
-a dense tensor.
+ step=(), or step=(None,), or step=(1,) to generate a csr output.
+ For other step parameter values, it falls back to slicing
+ a dense tensor.
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